Automatic procedures of
agrometeorological data
spatial interpolation for
the application of
simulation models
S. Orlandini(1), A. Dalla Marta(1), A. Cicogna(2)
(1)
DISAT - University of Florence
(2) ARPA - Friuli Venezia Giulia
• Agrometeorological variables are directly
involved in plant growth and development, but
also in the damages due to pests and diseases
• For this reason the knowledge and the
monitoring of such variable distribution and
variability on the territory represent the basis for
a correct management of agricultural activities
• The integration between spatial interpolation
methods and simulation models can provide
useful information disseminated to the farmers,
using texts, figures and maps
Minimum
temperature
Stable in time
Variable in space
Maximum
temperature
3
2
1
0
SRN
Variable in time
-1
-2
nov
Stable in space
-3
set
-4
lug
mag
mar
gen
mese
s
t
2
3
s
t
0
8
s
t
0
6
s
t
0
3
s
t
0
5
s
t
0
2
s
t
2
4
s
t
0
4
s
t
1
4
s
t
1
3
s
t
1
8
s
t
0
7
s
t
1
1
s
t
1
2
s
t
1
7
s
t
0
0
s
t
2
0
s
t
1
6
s
t
0
9
s
t
2
7
s
t
0
1
s
t
1
0
s
t
1
5
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9
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6
stazione
AIM
The aim of this work was the set up of an
integrated and automatic system for the
production of agrometeorological thematic maps
at farm level.
Maps provide information concerning weather
variables and the output of simulation models for
crop, pest and disease distribution on the
territory.
The system was created with the idea to directly
involve the final users.
The integrated system
•
•
•
•
•
Collection of agrometeorological and crop data
Elaboration of weather data
Spatial interpolation of data
Application of simulation models
Production of thematic maps of weather and
agrometeorological model output
• Information to the growers
The study was carried on in
the farm “Poggio Casciano”
located in the North part of
Chianti region (South of
Florence)
AVAILABLE DATA
• 40 weather stations for
temperature and relative
humidity
• 10 years of data
•Latitude and
longitude
•Altitude
•Aspect
•Distance from
valleys bottom
•Slope
THE AGROCLIMATIC
CLASSIFICATION
• Weather data were analysed to identify the climatic
characteristics of the farm areas.
• Different approaches were used, based on the
mean difference and the correlation coefficient.
• On these bases, three locations were identified as
the warmest, the coldest and the most
representative of the entire farm conditions.
• Standard agrometeorological stations were installed
in these locations.
COLDEST
WARMEST
REPRESENTATIVE
Station for
agroclimatic
monitoring
Station for
agrometeorological
monitoring
THE PROBLEM of LEAF WETNESS
• No standard for sensor positioning (top of the
canopy; within the canopy; north; south; etc.)
• No standard for measurement (0-1; 0-15; 0-100;
minutes; etc.)
• No standard for sensors design
• For this reason the use of LWD simulation
models, based on agrometeorological variables,
represents a valuable alternative to field
measurements.
• One literature review listed at least 16 models
capable of simulating surface wetness both with
empirical and physical approach.
SWEB model
• In this work a physical model based on the
energy balance was used. The model, developed
in the United States, was calibrated on
Sangiovese variety and transferred in Visual
Basic language to be part of the integrated
system
• The inputs are: Air temperature (°C), Relative
humidity (%), Wind Speed (m s-1), Precipitations
(mm), Net radiation (kW m-2)
• The output is the duration of leaf wetness in
minutes.
PLASMO
• PLASMO is a mathematical model set up in the
University of Florence and well validated for
Sangiovese variety.
• The model simulates the infection of Plasmopara
viticola through its principal stages of incubation,
sporulation, spore survival and inoculation,
jointly with the simulation of leaf area growth.
• The input are: air temperature (°C), relative
humidity (%), precipitations (mm), leaf wetness
(0-1)
Main
symptoms
It affects the leaves,
fruits and shoots.
When weather is
favourable and
protection against the
disease is not
provided, downy
mildew can easily
destroy 50-75% of
the production in one
season.
oil spot
on
upper
surface
of leaf
mildew
on
under
surface
of leaf
The integrated system
• The system was created using Visual Basic for
Application (Excel).
• The creation of grids and maps was done with
Surfer7.
• It is composed by different parts and the final
result is represented by both text files and
thematic maps of the most important
parameters.
1st MODULE
- Loading of weather and geo-topographical data
(“.csv”)
- Reading of weather and geo-topographical data
(creation of territorial matrices, control of data gaps)
- Spatial interpolation of weather data
- Calculation of solar radiation
2nd MODULE
- Simulation of leaf wetness
- Plasmo simulation
3rd MODULE
- Creation and export of thematic maps (“.png”)
- Creation of grids (“.grd”)
Distance from valleys bottom
Altitude
Correction factor based on the
deviations from the average of the 40
stations during the 10 years
Multiple regression with altitude and
distance from the valleys bottom to
interpolate temperature
Calculation of solar radiation.
The spatial variability is mainly due to slope and
aspect.
SWEB Model
Plasmo
Grid creation
Output “.txt”
Output “.grd”
Grid of relative humidity (day 157)
Output “.png”
Map of relative humidity (day 157)
Map of temperature (day 140)
Map of leaf wetness (day 153)
Map of number of current infections (day 154)
Map of number of days for the outbreak of the
current infection (day 154)
Available thematic maps
•
•
•
•
•
•
•
•
Minimum, maximum and mean temperature
Relative humidity
Leaf wetness
Global radiation, PAR
Number of current and total infections
Number of days for the outbreak of infections
Severity of each infection cycle
Area of infected and healthy leaf tissue
Future improvements
• Implementation of the system in a GIS and
creation of the related database
• Spatial interpolation of wind speed and
precipitation
• Personalization of the products and
application of other simulation models
• Creation of standard procedures to apply
agrometeorological monitoring at farm level
• Use of future scenarios for agronomical
purposes (shift of cultivation areas, increase of
early frost, etc.)